Practical Lab 2 - Data Visualization

CSCN8010 : Foundations of Machine Learning

• Name: Puneet Kaur
• ID: 8882343

#Import packages
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import plotly.express as px
import plotly.offline as pyo

#Plotly in notebook mode
pyo.init_notebook_mode()

Matplotlib Graph

Product Sales Bar Chart

The bar chart above displays the sales of different products, including laptops, smartphones, tablets, headphones, and cameras.

Product	Sales (in units)
Laptop	500
Smartphone	800
Tablet	300
Headphones	200
Camera	150

fig, ax = plt.subplots()

#Sample data
products = ['Laptop', 'Smartphone', 'Tablet', 'Headphones', 'Camera']
sales = [500, 800, 300, 200, 150]

#Bar Chart
ax.bar(products, sales)

#Customization
ax.set_ylabel('Sales (in units)')
ax.set_title('Product Sales Bar Chart')

plt.xticks(rotation=45)
plt.tight_layout()

#Display
plt.show()

Monthly Expenses Pie Chart

The pie chart displays the distribution of monthly expenses across food, housing, transportation, and entertainment.

#Sample data
categories = ['Food', 'Housing', 'Transportation', 'Entertainment']
expenses = [500, 1000, 300, 200]

#Pie Chart
plt.figure(figsize=(8, 6))
plt.pie(expenses, labels=categories, autopct='%1.1f%%', shadow=True, startangle=140)
plt.title('Monthly Expenses')
plt.axis('equal')

#Display
plt.show()

Seaborn Graph

Age Distribution by Passenger Class

The violin plot shows the age distribution of passengers in different classes on the Titanic.

Titanic Dataset:

#Laoding titanic dataset
titanic = sns.load_dataset('titanic')

#Violinplot
sns.set_theme(style="whitegrid")
plt.figure(figsize=(8, 6))
sns.violinplot(x='class', y='age', data=titanic, palette='Set3')

#Customization
plt.title('Age Distribution by Passenger Class')
plt.xlabel('Passenger Class')
plt.ylabel('Age')

#Display
plt.show()

House Prices by Neighborhood

The horizontal boxplot with observations shows house prices across different neighborhoods.

#Sample data
neighborhoods = ['A', 'B', 'C', 'A', 'B', 'C', 'A', 'B', 'C']
house_prices = [350000, 400000, 320000, 280000, 380000, 310000, 450000, 420000, 340000]

# Horizontal boxplot with observations using stripplot
plt.figure(figsize=(10, 6))
sns.boxplot(x=house_prices, y=neighborhoods, orient='h', palette='Set2')
sns.stripplot(x=house_prices, y=neighborhoods, orient='h', size=3, color='black', alpha=0.5)

# Customization
plt.title('House Prices by Neighborhood with Observations')
plt.xlabel('Price ($)')
plt.ylabel('Neighborhood')

#Display
plt.show()

Scatter Plot of Total Bill vs. Tip

The joint plot shows the relationship between the total bill and the tip amount in a restaurant.

Tips dataset:

#Import dataset
tips = sns.load_dataset("tips")

#Customization
sns.set_theme(style="darkgrid")

#Joinplot
g = sns.jointplot(x="total_bill", y="tip", data=tips,
                  kind="reg", truncate=False,
                  xlim=(0, 60), ylim=(0, 12),
                  color="m", height=7)

Plotly Express Graph

Temperature vs. Humidity: Interactive Scatter Plot

This dynamic scatter plot lets you explore how temperature and humidity vary in cities. Hover over points for city-specific details.

For more details, refer to the Plotly Express Documentation.

#Sample data
cities = ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Miami']
temperature = [68, 75, 62, 80, 84]
humidity = [52, 62, 58, 70, 76]

#Scatter plot with hover information
fig = px.scatter(x=temperature, y=humidity, text=cities, labels={'x': 'Temperature (°F)', 'y': 'Humidity (%)'})

fig.update_traces(marker=dict(size=12),
                  selector=dict(mode='markers+text'))

fig.update_layout(
    title='Temperature vs. Humidity',
)

#Display
fig.show()